Furthermore, despite existing datasets [1]}, [2]}, [3]}, [4]}, [5]}, [6]}, [7]}, [8]}, we notice an absence of objects of varying photometric challenges and perfect annotation labels, preventing current methods from being used for arbitrary object grasping.
Most importantly, previous datasets are not applicable to high-level tasks, like Robot-Environment Interaction, due to their limited perceptual views and single-scenario configurations.
To serve both basic and advanced manipulation tasks, we provide multi-view and multi-scene annotations with large-scale grasping labels based on two indoor datasets [9]}, [10]}, which comprise challenging objects of different photometric complexity.
Along with other off-the-shelf dense annotations, we believe the extended dataset can serve better for diverse grasping purposes.
